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Exploratory Data Analysis →easyMultiple ChoiceObjective-mapped

MLS-C01 Exploratory Data Analysis Practice Question

This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A data scientist is exploring a dataset and wants to check for missing values. Which method is most appropriate to identify the percentage of missing values per column?

A
Use Amazon S3 Select to query missing values
Why wrong: S3 Select is for retrieving subsets of data, not for computing missing percentages.
B
Use Amazon Athena to run a SELECT COUNT(*) query
Why wrong: Athena is more suited for SQL-based analysis but requires more setup.
C
Use Amazon QuickSight to create a missing value dashboard
Why wrong: QuickSight can visualize missing data but is not the most direct method for initial EDA.
D
Use AWS Glue Crawler to detect missing values
Why wrong: Glue Crawler infers schema and partitions, not missing values.
E
Use pandas .isnull().sum() in a SageMaker notebook
This is a direct and efficient way to count missing values per column.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use pandas .isnull().sum() in a SageMaker notebook

Using pandas .isnull().sum() in a SageMaker notebook is the most appropriate method because it directly provides the count (and thus the percentage when divided by total rows) of missing values per column, which is a standard exploratory data analysis technique. Option A is incorrect because Amazon S3 Select is used for filtering and retrieving subsets of data from S3 objects, not for computing missing values. Option B is incorrect because while Amazon Athena can run SQL queries like SELECT COUNT(*), it is less direct for per-column missing value analysis and requires a schema. Option C is incorrect because Amazon QuickSight is a visualization tool, not designed for programmatic missing value detection. Option D is incorrect because AWS Glue Crawler discovers schema and partitions, not missing values.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use Amazon S3 Select to query missing values
Why it's wrong here
S3 Select is for retrieving subsets of data, not for computing missing percentages.
✗
Use Amazon Athena to run a SELECT COUNT(*) query
Why it's wrong here
Athena is more suited for SQL-based analysis but requires more setup.
✗
Use Amazon QuickSight to create a missing value dashboard
Why it's wrong here
QuickSight can visualize missing data but is not the most direct method for initial EDA.
✗
Use AWS Glue Crawler to detect missing values
Why it's wrong here
Glue Crawler infers schema and partitions, not missing values.
✓
Use pandas .isnull().sum() in a SageMaker notebook
Why this is correct
This is a direct and efficient way to count missing values per column.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use pandas .isnull().sum() in a SageMaker notebook — Using pandas .isnull().sum() in a SageMaker notebook is the most appropriate method because it directly provides the count (and thus the percentage when divided by total rows) of missing values per column, which is a standard exploratory data analysis technique. Option A is incorrect because Amazon S3 Select is used for filtering and retrieving subsets of data from S3 objects, not for computing missing values. Option B is incorrect because while Amazon Athena can run SQL queries like SELECT COUNT(*), it is less direct for per-column missing value analysis and requires a schema. Option C is incorrect because Amazon QuickSight is a visualization tool, not designed for programmatic missing value detection. Option D is incorrect because AWS Glue Crawler discovers schema and partitions, not missing values.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.